Objectives Aiming at the challenges of special scenarios and scarce public data in carrier aircraft deck operations, this study proposes a multi-dimensional feature-based recognition method for carrier aircraft deck operations.

Methods Firstly, key points such as channel boundaries and static obstacles are accurately selected to represent the environmental information, and the interactions between dynamic individuals and static environmental objects are modelled by graph convolutional networks, to deeply explore the potential connections of operational object interactions. Then, a multi-scale spatio-temporal feature extraction module is designed to introduce the dilated attention mechanism, which focuses on the key individual interactions in global and local space by setting different dilation rates; at the same time, temporal sequential convolutional networks (TCN) and the attention mechanism are used to extract the interaction features between individuals in the temporal dimension, so as to efficiently capture the dynamic relationships between individuals in the long and short sequences. Finally, the multi-scale spatio-temporal feature extraction module is stacked multiple times to adaptively extract multi-dimensional feature, thereby improving the recognition accuracy of carrier aircraft deck operations.

Results Experimental results show that, on a self-built dataset of heterogeneous object carrier aircraft deck operation recognition from different perspectives, the proposed method significantly outperforms group activity recognition methods such as ARG, DIN, AT, and GroupFormer in terms of accuracy, achieving a recognition precision of 97.8%.

Conclusions This work can provide reference for high-precision recognition of carrier aircraft deck operations.